import numpy as np
import pandas as pd
df = pd.read_csv('../../Data/01CrudoNoEditar/01desastres_crudo.csv', delimiter=';', encoding='latin-1')
df.head(3)
C:\Users\blanc\AppData\Local\Temp\ipykernel_3912\2290156345.py:1: DtypeWarning: Columns (18,24,25,45) have mixed types. Specify dtype option on import or set low_memory=False.
df = pd.read_csv('../../Data/01CrudoNoEditar/01desastres_crudo.csv', delimiter=';', encoding='latin-1')
| Dis No | Year | Seq | Glide | Disaster Group | Disaster Subgroup | Disaster Type | Disaster Subtype | Disaster Subsubtype | Event Name | ... | Reconstruction Costs, Adjusted ('000 US$) | Insured Damages ('000 US$) | Insured Damages, Adjusted ('000 US$) | Total Damages ('000 US$) | Total Damages, Adjusted ('000 US$) | CPI | Adm Level | Admin1 Code | Admin2 Code | Geo Locations | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1900-9002-CPV | 1900 | 9002 | NaN | Natural | Climatological | Drought | Drought | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 2,849084409 | NaN | NaN | NaN | NaN |
| 1 | 1900-9001-IND | 1900 | 9001 | NaN | Natural | Climatological | Drought | Drought | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 2,849084409 | NaN | NaN | NaN | NaN |
| 2 | 1902-0012-GTM | 1902 | 12 | NaN | Natural | Geophysical | Earthquake | Ground movement | NaN | NaN | ... | NaN | NaN | NaN | 25000.0 | 843726.0 | 2,963047785 | NaN | NaN | NaN | NaN |
3 rows × 50 columns
df.shape
(16636, 50)
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 16636 entries, 0 to 16635
Data columns (total 50 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Dis No 16636 non-null object
1 Year 16636 non-null int64
2 Seq 16636 non-null int64
3 Glide 1736 non-null object
4 Disaster Group 16636 non-null object
5 Disaster Subgroup 16636 non-null object
6 Disaster Type 16636 non-null object
7 Disaster Subtype 13313 non-null object
8 Disaster Subsubtype 1117 non-null object
9 Event Name 3969 non-null object
10 Country 16636 non-null object
11 ISO 16636 non-null object
12 Region 16636 non-null object
13 Continent 16636 non-null object
14 Location 14825 non-null object
15 Origin 4085 non-null object
16 Associated Dis 3593 non-null object
17 Associated Dis2 763 non-null object
18 OFDA Response 1716 non-null object
19 Appeal 2559 non-null object
20 Declaration 3343 non-null object
21 AID Contribution ('000 US$) 776 non-null float64
22 Dis Mag Value 5064 non-null float64
23 Dis Mag Scale 15416 non-null object
24 Latitude 2775 non-null object
25 Longitude 2775 non-null object
26 Local Time 1156 non-null object
27 River Basin 1336 non-null object
28 Start Year 16636 non-null int64
29 Start Month 16241 non-null float64
30 Start Day 13021 non-null float64
31 End Year 16636 non-null int64
32 End Month 15936 non-null float64
33 End Day 13105 non-null float64
34 Total Deaths 11838 non-null float64
35 No Injured 4147 non-null float64
36 No Affected 9673 non-null float64
37 No Homeless 2470 non-null float64
38 Total Affected 12143 non-null float64
39 Reconstruction Costs ('000 US$) 38 non-null float64
40 Reconstruction Costs, Adjusted ('000 US$) 36 non-null float64
41 Insured Damages ('000 US$) 1109 non-null float64
42 Insured Damages, Adjusted ('000 US$) 1109 non-null float64
43 Total Damages ('000 US$) 5384 non-null float64
44 Total Damages, Adjusted ('000 US$) 5366 non-null float64
45 CPI 16530 non-null object
46 Adm Level 8475 non-null object
47 Admin1 Code 5030 non-null object
48 Admin2 Code 4248 non-null object
49 Geo Locations 8475 non-null object
dtypes: float64(17), int64(4), object(29)
memory usage: 6.3+ MB
from ydata_profiling import ProfileReport
df = pd.read_csv('../../Data/01CrudoNoEditar/01desastres_crudo.csv', delimiter=';', encoding='latin-1')
profile = ProfileReport(df, title='Pandas Profiling Report')
profile
C:\Users\blanc\AppData\Local\Temp\ipykernel_3912\3046761109.py:3: DtypeWarning: Columns (18,24,25,45) have mixed types. Specify dtype option on import or set low_memory=False.
df = pd.read_csv('../../Data/01CrudoNoEditar/01desastres_crudo.csv', delimiter=';', encoding='latin-1')
Summarize dataset: 0%| | 0/5 [00:00<?, ?it/s]
Generate report structure: 0%| | 0/1 [00:00<?, ?it/s]
Render HTML: 0%| | 0/1 [00:00<?, ?it/s]